Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
A taxonomy of distributed termination detection algorithms
Journal of Systems and Software
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Parallelizing a convergent approximate inference method
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Database foundations for scalable RDF processing
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
PowerGraph: distributed graph-parallel computation on natural graphs
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
Data Parallel Implementation of Belief Propagation in Factor Graphs on Multi-core Platforms
International Journal of Parallel Programming
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As computer clusters become more common and the size of the problems encountered in the field of AI grows, there is an increasing demand for efficient parallel inference algorithms. We consider the problem of parallel inference on large factor graphs in the distributed memory setting of computer clusters. We develop a new efficient parallel inference algorithm, DBRSplash, which incorporates over-segmented graph partitioning, belief residual scheduling, and uniform work Splash operations. We empirically evaluate the DBRSplash algorithm on a 120 processor cluster and demonstrate linear to super-linear performance gains on large factor graph models.